Blockchain is a revolutionary technology that is making a great impact on modern society due to its transparency, decentralization, and security properties. Blockchain gained considerable attention due to its very first application of Cryptocurrencies e.g., Bitcoin. In the near future, Blockchain technology is determined to transform the way we live, interact, and perform businesses. Recently, academics, industrialists, and researchers are aggressively investigating different aspects of Blockchain as an emerging technology. Unlike other Blockchain surveys focusing on either its applications, challenges, characteristics, or security, we present a comprehensive survey of Blockchain technology's evolution, architecture, development frameworks, and security issues. We also present a comparative analysis of frameworks, classification of consensus algorithms, and analysis of security risks & cryptographic primitives that have been used in the Blockchain so far. Finally, this paper elaborates on key future directions, novel use cases and open research challenges, which could be explored by researchers to make further advances in this field.

Disabled people encounter many barriers while attempting to access the services on the web. Nevertheless, many tools, which could help them to access the web, are available. E-commerce websites have been also intensively and widely used. The e-commerce market in Saudi Arabia will hit $13.3 billion by 2015. This huge investment requires e-commerce websites to be accessible by different types of users. This paper explores the tools that usually used by disabled users while using the web. It also discusses a number of available tools that help designers, developers and testers to assess web accessibility. It also evaluates the accessibility of 3 popular Arab ecommerce websites using 5 accessibility testing tools; namely Achecker, TAW, Eval Access, MAUVE and FAE. This research has found that Most accessibility guidelines are covered by Achecker tool. Navigation, readability, input assistance and timing are the common found accessibility problems while assessing the accessibility of the targeted websites. It has been also revealed that HTML can influence accessibility evaluation as HTML errors are considered as accessibility problems. It has been clearly observed that improvements are needed for better web accessibility, although some tools did report a small number of accessibility problems for some websites.

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Systems' usability is one of the critical attribute of any system's quality. Medical practitioners usually encounter usability difficulties while using a health information system like other systems. There are different usability factors, which are expected to influence systems' usability. Errors preventions, patient safety and privacy are vital usability factors and should not be ignored while developing a health information system. This study is based on a comprehensive analysis of published academic and industrial literature to provide the current status of health information systems' usability. It also identifies different usability factors such as privacy, errors, design and efficiency. Usability factors are then assessed. Those factors are further examined through a questionnaire to study the priorities of them from medical practitioners' point of view in Saudi Arabia. The statistical analysis shows that the privacy and errors are very critical than the other usability factors. The study results further revealed that availability and response time are the main challenges faced by the medical practitioners when using the HIS. However, flexibility and customizability were claimed to ease the use of the HIS. In addition, a number of statistical correlations were established. Overall, the study findings seemed helpful to designers and implementers to consider these factors for successful implementation of HIS.

Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.

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Enterprise Resource Planning (ERP) is a frequently used system among organizations to automate their workflows, and companies’ performances are highly dependent on the ERP system. The usability issues of ERP systems may cause performance degradation, resulting in the company’s loss in terms of cost. Previously, several studies reported many usability problems of ERP systems. It can be helpful for the developers and designers of ERP systems to use design recommendations as a quick reference to avoid recurrent usability problems of ERP systems. Currently, this area lacks effective consolidation of the previously reported usability problems data. This paper presents a unique approach to developing a precise checklist of ERP usability problems using the topic modeling technique. Our analysis found six different usability problem-related topics that can be generalized for various ERP systems. We have successfully validated our checklist in three different usability studies of ERP systems. The most found usability problems are “difficulty searching and finding desired item/information in interface and error handling” and “missing data and information”. The outcome of our paper is the provision of recommendations to avoid the usability problems of ERP systems and help organizations efficiently prevent frequent issues during the development and maintenance of ERP systems.

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims' requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the